An exploration of EEG features during recovery following stroke - implications for BCI-mediated neurorehabilitation therapy.

BACKGROUND: Brain-Computer Interfaces (BCI) can potentially be used to aid in the recovery of lost motor control in a limb following stroke. BCIs are typically used by subjects with no damage to the brain therefore relatively little is known about the technical requirements for the design of a rehabilitative BCI for stroke. METHODS: 32-channel electroencephalogram (EEG) was recorded during a finger-tapping task from 10 healthy subjects for one session and 5 stroke patients for two sessions approximately 6 months apart. An off-line BCI design based on Filter Bank Common Spatial Patterns (FBCSP) was implemented to test and compare the efficacy and accuracy of training a rehabilitative BCI with both stroke-affected and healthy data. RESULTS: Stroke-affected EEG datasets have lower 10-fold cross validation results than healthy EEG datasets. When training a BCI with healthy EEG, average classification accuracy of stroke-affected EEG is lower than the average for healthy EEG. Classification accuracy of the late session stroke EEG is improved by training the BCI on the corresponding early stroke EEG dataset. CONCLUSIONS: This exploratory study illustrates that stroke and the accompanying neuroplastic changes associated with the recovery process can cause significant inter-subject changes in the EEG features suitable for mapping as part of a neurofeedback therapy, even when individuals have scored largely similar with conventional behavioural measures. It appears such measures can mask this individual variability in cortical reorganization. Consequently we believe motor retraining BCI should initially be tailored to individual patients.

A novel BCI-controlled pneumatic glove system for home-based neurorehabilitation.

Commercially available devices for Brain-Computer Interface (BCI)-controlled robotic stroke rehabilitation are prohibitively expensive for many researchers who are interested in the topic and physicians who would utilize such a device. Additionally, they are cumbersome and require a technician to operate, increasing the inaccessibility of such devices for home-based robotic stroke rehabilitation therapy. Presented here is the design, implementation and test of an inexpensive, portable and adaptable BCI-controlled hand therapy device. The system utilizes a soft, flexible, pneumatic glove which can be used to deflect the subject's wrist and fingers. Operation is provided by a custom-designed pneumatic circuit. Air flow is controlled by an embedded system, which receives serial port instruction from a PC running real-time BCI software. System tests demonstrate that glove control can be successfully driven by a real-time BCI. A system such as the one described here may be used to explore closed loop neurofeedback rehabilitation in stroke relatively inexpensively and potentially in home environments.

Prefrontal cortical activation during arithmetic processing differentiated by cultures: a preliminary fNIRS study.

Understanding the neural basis of arithmetic processes could play an important role in improving mathematical education. This study investigates the prefrontal cortical activation among subjects from different cultural backgrounds while performing two difficulty levels of mental arithmetic tasks. The prefrontal cortical activation is measured using a high density 206 channels fNIRS. 8 healthy subjects, consisting of 5 Asians and 3 Europeans, are included in this study. NIRS-SPM is used to compute hemoglobin response changes and generate brain activation map based on two contrasts defined as Easy versus Rest and Hard versus Rest. Differences between the Asian group and the European group are found in both contrasts of Easy versus Rest and Hard versus Rest. The results suggest people with different cultural backgrounds engage different neural pathways during arithmetic processing.

Functional Near Infrared Spectroscopy (fNIRS) synthetic data generation.

Accurately modelled computer-generated data can be used in place of real-world signals for the design, test and validation of signal processing techniques in situations where real data is difficult to obtain. Bio-signal processing researchers interested in working with fNIRS data are restricted due to the lack of freely available fNIRS data and by the prohibitively expensive cost of fNIRS systems. We present a simplified mathematical description and associated MATLAB implementation of model-based synthetic fNIRS data which could be used by researchers to develop fNIRS signal processing techniques. The software, which is freely available, allows users to generate fNIRS data with control over a wide range of parameters and allows for fine-tuning of the synthetic data. We demonstrate how the model can be used to generate raw fNIRS data similar to recorded fNIRS signals. Signal processing steps were then applied to both the real and synthetic data. Visual comparisons between the temporal and spectral properties of the real and synthetic data show similarity. This paper demonstrates that our model for generating synthetic fNIRS data can replicate real fNIRS recordings.

A novel co-locational and concurrent fNIRS/EEG measurement system: design and initial results.

We describe here the design, set-up and first time classification results of a novel co-locational functional Near-Infrared Spectroscopy/Electroencephalography (fNIRS/EEG) recording device suitable for brain computer interfacing applications using neural-hemodynamic signals. Our dual-modality system recorded both hemodynamic and electrical activity at seven sites over the motor cortex during an overt finger-tapping task. Data was collected from two subjects and classified offline using Linear Discriminant Analysis (LDA) and Leave-One-Out Cross-Validation (LOOCV). Classification of fNIRS features, EEG features and a combination of fNIRS/EEG features were performed separately. Results illustrate that classification of the combined fNIRS/EEG feature space offered average improved performance over classification of either feature space alone. The complementary nature of the physiological origin of the dual measurements offer a unique and information rich signal for a small measurement area of cortex. We feel this technology may be particularly useful in the design of BCI devices for the augmentation of neurorehabilitation therapy.

Intelligent artifact classification for ambulatory physiological signals.

Connected health represents an increasingly important model for health-care delivery. The concept is heavily reliant on technology and in particular remote physiological monitoring. One of the principal challenges is the maintenance of high quality data streams which must be collected with minimally intrusive, inexpensive sensor systems operating in difficult conditions. Ambulatory monitoring represents one of the most challenging signal acquisition challenges of all in that data is collected as the patient engages in normal activities of everyday living. Data thus collected suffers from considerable corruption as a result of artifact, much of it induced by motion and this has a bearing on its utility for diagnostic purposes. We propose a model for ambulatory signal recording in which the data collected is accompanied by labeling indicating the quality of the collected signal. As motion is such an important source of artifact we demonstrate the concept in this case with a quality of signal measure derived from motion sensing technology viz. accelerometers. We further demonstrate how different types of artifact might be tagged to inform artifact reduction signal processing elements during subsequent signal analysis. This is demonstrated through the use of multiple accelerometers which allow the algorithm to distinguish between disturbance of the sensor relative to the underlying tissue and movement of this tissue. A brain monitoring experiment utilizing EEG and fNIRS is used to illustrate the concept.